Technological innovations are fuelled by the current needs of the industry. As 2020 is coming to an end, we are looking at a considerable enhancement in technological tools and practices. There was a need for a sturdier infrastructure and more resilient practices that gave way to further computing advancements, and Alibaba Cloud presented itself as an integrated in-house one-stop solution for a variety of cloud computing solutions.
A world recovering from economic instability needed an anchor and new technology was the perfect anchor. The effects of the COVID-19 pandemic could have been much worse, but technology stopped the world from coming to a stand-still.
Since 2009, DevOps has outgrown almost any other SDLC practice. Organizations have accepted it as the option for the future, as the potential is unlimited and the evolution is stable.
This blog focuses on the present and future of DevOps practice. The blog will split the different aspects of the DevOps pipeline to showcase module-based changes that are on-going and expected in the future. The complete practice I base my post on is using the Alibaba Cloud DevOps solution that includes Container Service for Kubernetes (ACK), Microservices, Docker, and tools like Jenkins for CI.
DevOps is known to accelerate the total build to deploy lifecycle. It uses automation to accelerate the total process. DevOps leverages Infrastructure as Code (IaC) and implements Continuous Integration and Continuous Delivery for software releases. On the other hand, DataOps aims to reduce the time taken for data analytics. The total process focuses on cutting down the time from strategy to modeling, which will lead to the extraction of more value from the project. All-in-all, DataOps combines the functionality and practices of DevOps, analytics, and Agile development to curate a solution that extracts value.
Any solution that extracts more value from a setup is usually the go-to solution. By utilizing Agile methodology, an organization enables frequent and quick responses for any customer requirements. DevOps has the ground with supreme agility and ease of communication, and DataOps adds a data pipeline into the mix to facilitate a parallel channel for analytics.
Machine Learning utilizes a great deal of data metrics and reports indicating requirements. Moving forward, Machine Learning will play an important role in determining performance, bandwidth, and also for risk assessment. DevOps and Machine Learning coming together can help shorten the SDLC curve.
DevOps gives way to the implementation of Machine Learning and Artificial Intelligence. These can be utilized to assess different scenarios of development and deployment. Even before the software is released, DevOps teams can have viable data on their hands. This data will enhance the software quality by outlining any low-performing code fragment, service, and scenario. ML for AI can enable organizations to come up with better solutions based on accurate data.
User interaction, code, testing reports, real-world analysis data, performance, and production are the end-points of any development-deployment cycle. If there is unique data available for your software at all of these points, continuous integration and delivery can be faster. From a developer’s perspective, the analysis of code based on reports generated through the implementation of ML can lead to a focused debugging approach.
The Continuous Testing methodology suggests automation at a level that drives the core DevOps practice. Artificial Intelligence can drive these core values without any human interaction with the system. Integration of AI can minimize control-stop structure and maximize productivity. Granting AI-based provisioning could be another step to a future where IAC Infrastructure can further be scaled accordingly. Alibaba Cloud Auto Scaling has the functionality to gather data and scale resources to maintain a stable system.
Organizations need to embrace evolution to make shifts with their business strategy that complement the changing world. Thoughts must be given to a more streamlined, efficient, and productive software delivery. Automation enables faster integration and delivery, but emphasis must be provided to the whole system before automating the modules.
An application may be made up of thousands of modules and microservices working together. Building efficient applications to reduce the number of applications could give automation and productivity the boost it needs. Automation is dependent on where your market is going, how much feedback you receive from clients, and how well you integrate security services.
Automation must be maximized in the continuous integration and delivery cycle, testing, and QA. Using proper tools to maintain or maximize these cycles has to be the common goal between teams.
DevOps is shifting towards 100% automation. The future of DevOps is highly dependent on more automated stages.
Security is a term that induces competence and confidence in a practice. Computing and security are the two that run parallel to each other. With the evolution of computing technology, security issues have also evolved. Innovation runs both ways; new technologies and security threats run concurrently.
Alibaba Cloud has a strong security product suite. Some of these products are automatically deployed with your DevOps pipeline. When deploying with Alibaba Cloud Terraform, it includes support for Resource and Access Management (RAM) for service and user authorization. Alibaba Cloud Firewall and Anti-DDoS products have a long lineage of securing deployments.
DevOps + Security = DevSecOps, An integration of security practices from a grassroots level. Today, a lot depends on containers, but what does the future hold?
DevOps as a culture will spread into unchartered territories. Different types of organizations will spread their reach and adopt DevOps. Alibaba Cloud’s infrastructure will help organizations adopt and adapt to these unchartered territories. Security practices need to grow for a solution to perform under the scope of evolving systems.
DevSecOps is a fairly new practice. It is an idea that needs time to filter out what’s needed and what’s not. Security and privacy are two separate mechanisms that will need addressing. Despite the heavy compliance and security standards already in place with DevOps, security is still a secondary practice that applies only after release. This must change. DevSecOps streams that idea, and it has to be implemented as a standard practice.
In Part 2 of this blog series, I will discuss my thoughts on:
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